class_docstr: Constructs a Deformable DETR feature extractor. Differs only in the postprocessing of object detection compared to
  DETR.
  
  This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
  should refer to this superclass for more information regarding those methods.
  
  Args:
      format (`str`, *optional*, defaults to `"coco_detection"`):
          Data format of the annotations. One of "coco_detection" or "coco_panoptic".
      do_resize (`bool`, *optional*, defaults to `True`):
          Whether to resize the input to a certain `size`.
      size (`int`, *optional*, defaults to 800):
          Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
          sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
          the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
          height / width, size)`.
      max_size (`int`, *optional*, defaults to 1333):
          The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is
          set to `True`.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether or not to normalize the input with mean and standard deviation.
      image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
          The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
      image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
          The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
          ImageNet std.
code: def prepare_coco_panoptic(
      self, image, target, masks_path, return_masks=True
  ):
      w, h = image.size
      ann_info = target.copy()
      ann_path = pathlib.Path(masks_path) / ann_info["file_name"]
  
      if "segments_info" in ann_info:
          masks = np.asarray(Image.open(ann_path), dtype=np.uint32)
          masks = rgb_to_id(masks)
  
          ids = np.array(
              [ann["id"] for ann in ann_info["segments_info"]]
          )
          masks = masks == ids[:, None, None]
          masks = np.asarray(masks, dtype=np.uint8)
  
          labels = np.asarray(
              [ann["category_id"] for ann in ann_info["segments_info"]],
              dtype=np.int64,
          )
  
      target = {}
      target["image_id"] = np.asarray(
          [
              ann_info["image_id"]
              if "image_id" in ann_info
              else ann_info["id"]
          ],
          dtype=np.int64,
      )
      if return_masks:
          target["masks"] = masks
      target["class_labels"] = labels
  
      target["boxes"] = masks_to_boxes(masks)
  
      target["size"] = np.asarray([int(h), int(w)], dtype=np.int64)
      target["orig_size"] = np.asarray([int(h), int(w)], dtype=np.int64)
      if "segments_info" in ann_info:
          target["iscrowd"] = np.asarray(
              [ann["iscrowd"] for ann in ann_info["segments_info"]],
              dtype=np.int64,
          )
          target["area"] = np.asarray(
              [ann["area"] for ann in ann_info["segments_info"]],
              dtype=np.float32,
          )
  
      return image, target
  
imports: import pathlib
  import warnings
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  from PIL import Image
  from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
  from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
  from ...utils import TensorType, is_torch_available, logging
label_desc: Use image node_id 6 for calling the DeformableDetrFeatureExtractor obj's underlying member method code with example usage: obj.prepare_coco_panoptic(image, target, masks_path, return_masks) and returns: image,  target